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https://github.com/helblazer811/ManimML.git
synced 2025-05-20 20:16:32 +08:00
Added padding.
Fixed a bug with ImageLayerToConvolutional2D Padding example
This commit is contained in:
Alec Helbling
@ -12,6 +12,89 @@ from manim_ml.neural_network.layers.parent_layers import (
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)
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from manim_ml.gridded_rectangle import GriddedRectangle
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class FeatureMap(VGroup):
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"""Class for making a feature map"""
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def __init__(
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self,
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color=ORANGE,
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feature_map_size=None,
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fill_color=ORANGE,
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fill_opacity=0.2,
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cell_width=0.2,
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padding=(0, 0),
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stroke_width=2.0,
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show_grid_lines=False,
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padding_dashed=False
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):
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super().__init__()
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self.color = color
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self.feature_map_size = feature_map_size
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self.fill_color = fill_color
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self.fill_opacity = fill_opacity
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self.cell_width = cell_width
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self.padding = padding
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self.stroke_width = stroke_width
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self.show_grid_lines = show_grid_lines
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self.padding_dashed = padding_dashed
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# Check if we have non-zero padding
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if padding[0] > 0 or padding[1] > 0:
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# Make the exterior rectangle dashed
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width_with_padding = (self.feature_map_size[0] + self.padding[0] * 2) * self.cell_width
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height_with_padding = (self.feature_map_size[1] + self.padding[1] * 2) * self.cell_width
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self.untransformed_width = width_with_padding
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self.untransformed_height = height_with_padding
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self.exterior_rectangle = GriddedRectangle(
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color=self.color,
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width=width_with_padding,
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height=height_with_padding,
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fill_color=self.color,
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fill_opacity=self.fill_opacity,
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stroke_color=self.color,
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stroke_width=self.stroke_width,
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grid_xstep=self.cell_width,
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grid_ystep=self.cell_width,
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grid_stroke_width=self.stroke_width / 2,
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grid_stroke_color=self.color,
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show_grid_lines=self.show_grid_lines,
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dotted_lines=self.padding_dashed
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)
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self.add(self.exterior_rectangle)
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# Add an interior rectangle with no fill color
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self.interior_rectangle = GriddedRectangle(
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color=self.color,
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fill_opacity=0.0,
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width=self.feature_map_size[0] * self.cell_width,
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height=self.feature_map_size[1] * self.cell_width,
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stroke_width=self.stroke_width
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)
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self.add(self.interior_rectangle)
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else:
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# Just make an exterior rectangle with no dashes.
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self.untransformed_height = self.feature_map_size[1] * self.cell_width,
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self.untransformed_width = self.feature_map_size[0] * self.cell_width,
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# Make the exterior rectangle
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self.exterior_rectangle = GriddedRectangle(
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color=self.color,
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height=self.feature_map_size[1] * self.cell_width,
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width=self.feature_map_size[0] * self.cell_width,
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fill_color=self.color,
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fill_opacity=self.fill_opacity,
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stroke_color=self.color,
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stroke_width=self.stroke_width,
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grid_xstep=self.cell_width,
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grid_ystep=self.cell_width,
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grid_stroke_width=self.stroke_width / 2,
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grid_stroke_color=self.color,
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show_grid_lines=self.show_grid_lines,
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)
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self.add(self.exterior_rectangle)
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def get_corners_dict(self):
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"""Returns a dictionary of the corners"""
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# Sort points through clockwise rotation of a vector in the xy plane
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return self.exterior_rectangle.get_corners_dict()
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class Convolutional2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
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"""Handles rendering a convolutional layer for a nn"""
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@ -24,33 +107,48 @@ class Convolutional2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
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cell_width=0.2,
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filter_spacing=0.1,
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color=BLUE,
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pulse_color=ORANGE,
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show_grid_lines=False,
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active_color=ORANGE,
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filter_color=ORANGE,
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show_grid_lines=False,
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fill_opacity=0.3,
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stride=1,
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stroke_width=2.0,
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activation_function=None,
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padding=0,
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padding_dashed=True,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.num_feature_maps = num_feature_maps
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self.filter_color = filter_color
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if isinstance(padding, tuple):
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assert len(padding) == 2
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self.padding = padding
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elif isinstance(padding, int):
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self.padding = (padding, padding)
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else:
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raise Exception(f"Unrecognized type for padding: {type(padding)}")
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if isinstance(feature_map_size, int):
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self.feature_map_size = (feature_map_size, feature_map_size)
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else:
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self.feature_map_size = feature_map_size
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if isinstance(filter_size, int):
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self.filter_size = (filter_size, filter_size)
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else:
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self.filter_size = filter_size
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self.cell_width = cell_width
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self.filter_spacing = filter_spacing
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self.color = color
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self.pulse_color = pulse_color
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self.active_color = active_color
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self.stride = stride
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self.stroke_width = stroke_width
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self.show_grid_lines = show_grid_lines
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self.activation_function = activation_function
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self.fill_opacity = fill_opacity
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self.padding_dashed = padding_dashed
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def construct_layer(
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self,
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@ -92,12 +190,14 @@ class Convolutional2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
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# Draw rectangles that are filled in with opacity
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feature_maps = []
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for filter_index in range(self.num_feature_maps):
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rectangle = GriddedRectangle(
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# Check if we need to add padding
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"""
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feature_map = GriddedRectangle(
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color=self.color,
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height=self.feature_map_size[1] * self.cell_width,
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width=self.feature_map_size[0] * self.cell_width,
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fill_color=self.color,
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fill_opacity=0.2,
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fill_opacity=self.fill_opacity,
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stroke_color=self.color,
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stroke_width=self.stroke_width,
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grid_xstep=self.cell_width,
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@ -106,52 +206,44 @@ class Convolutional2DLayer(VGroupNeuralNetworkLayer, ThreeDLayer):
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grid_stroke_color=self.color,
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show_grid_lines=self.show_grid_lines,
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)
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"""
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# feature_map = GriddedRectangle()
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feature_map = FeatureMap(
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color=self.color,
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feature_map_size=self.feature_map_size,
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cell_width=self.cell_width,
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fill_color=self.color,
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fill_opacity=self.fill_opacity,
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padding=self.padding,
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padding_dashed=self.padding_dashed
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)
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# Move the feature map
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rectangle.move_to([0, 0, filter_index * self.filter_spacing])
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feature_map.move_to([0, 0, filter_index * self.filter_spacing])
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# rectangle.set_z_index(4)
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feature_maps.append(rectangle)
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feature_maps.append(feature_map)
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return VGroup(*feature_maps)
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def highlight_and_unhighlight_feature_maps(self):
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"""Highlights then unhighlights feature maps"""
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return Succession(
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ApplyMethod(self.feature_maps.set_color, self.pulse_color),
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ApplyMethod(self.feature_maps.set_color, self.active_color),
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ApplyMethod(self.feature_maps.set_color, self.color),
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)
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def make_forward_pass_animation(
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self, run_time=5, corner_pulses=False, layer_args={}, **kwargs
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self, run_time=5, layer_args={}, **kwargs
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):
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"""Convolution forward pass animation"""
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# Note: most of this animation is done in the Convolution3DToConvolution3D layer
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if corner_pulses:
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raise NotImplementedError()
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passing_flashes = []
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for line in self.corner_lines:
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pulse = ShowPassingFlash(
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line.copy().set_color(self.pulse_color).set_stroke(opacity=1.0),
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time_width=0.5,
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run_time=run_time,
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rate_func=rate_functions.linear,
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)
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passing_flashes.append(pulse)
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# per_filter_run_time = run_time / len(self.feature_maps)
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# Make animation group
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if not self.activation_function is None:
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animation_group = AnimationGroup(
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*passing_flashes,
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# filter_flashes
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self.activation_function.make_evaluate_animation(),
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self.highlight_and_unhighlight_feature_maps(),
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lag_ratio=0.0,
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)
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else:
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if not self.activation_function is None:
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animation_group = AnimationGroup(
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self.activation_function.make_evaluate_animation(),
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self.highlight_and_unhighlight_feature_maps(),
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lag_ratio=0.0,
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)
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else:
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animation_group = AnimationGroup()
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animation_group = AnimationGroup()
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return animation_group
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